bachmann monitoring gmbh
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Bachmann Monitoring GmbH
Conditioning Monitoring System
(CMS)
Dr. Steffen BiehlM.Sc. Peter Collmann 09.08.2018
Who is AGF Energia & Neo Wind
Since 2016 Neo Wind and AGF Energia have worked together to offer to the market the best solutions.
Neo Wind's technical expertise in renewable projects allied with AGF Energia's multidisciplinary project implementation and O&M experience provides the best solutions and innovations for its customers, always seeking quality, efficiency and cost reduction in its services.
RJPR
CE
Headquarter: Rudolstadt (Federal State of Thuringia)
Established: July 1998 (as µ-Sen GmbH) 20 years of experience in wind energy
Single source: CMS Hardware & Software
Remote Monitoring ServicesTraining
Sensor technologies
Staff: 62
Owner: 100% Bachmann electronic GmbH Feldkirch since 01.01.2011
Who is Bachmann Monitoring GmbH (BAM)
Knowledge Based Maintenance OptimisationBachmann Monitoring GmbH
Maintenance Optimisation
Run to Failure
Maintenance Optimisation
Breakdown Maintenance
Preventive Maintenance
Reactive Maintenance
Predictive Maintenance
Time-based Maintenance
Total Productive Maintenance
Scheduled Maintenance
Firefighting
Reliability Centred Maintenance
On Condition Maintenance
…
Maintenance Strategies
Only Three Strategies:
Reactive Maintenance (Run to Failure)
Run the machine until it fails – high repair costs, poor availability
Predictive Maintenance (Condition Based)
Monitoring of selected parameters to assess the condition of the machine so that maintenance can be planned.
Preventive Maintenance (Time-based)
Run the plant for a pre-determined period then overhaul
Knowledge Based Maintenance OptimisationBachmann Monitoring GmbH
Knowledge based maintenanceChoose your Strategy
Preventive Maintenance
Choose repair time based on interval
Good partsreplaced
Maintenance induced problems
Some parts will failearly – CM neededfor critical items
Can be augmentedwith equivalentoperating hours
Reactive Maintenance Predictive maintenance Optimised Maintenance
Repair item on failure
Unplanneddowntime
Consequentialdamage
Fine forconsumables in non critical plant
Requires ConditionIndication
Failure indicatormust give sufficientlead time
Detectionprobability must behigh
Plant must haveopportunities formaintenance
Strategy based on plant item
Reducesunnecessary work
Reduced unplannedunavailability
Supports planningfor major items
Inputs from Big Data methods toimproveprognostics
Knowledge Based Maintenance OptimisationBachmann Monitoring GmbH
Knowledge based maintenanceChoose your Strategy
Preventive Maintenance
Operating Informationnecessary
Knowledge ofreliabilty generated
Fewer Actions, but some unnecessaryand most too early
Reactive Maintenance Predictive maintenance Optimised Maintenance
Little Informationneeded
Little Knowledgegenerated
Lots of Actionsrequired
Large amount ofInformationgenerated
Expert analysis toconvert toKnowledge
Actions at optimumtime, but not applicable to all plant items
Collects and Collates multiple Information sources
Knowledge usedand updatedcontinuously
Minimum amountof Action tomaintain plant health
Cost Risk
Knowledge Based Maintenance OptimisationBachmann Monitoring GmbH
Knowledge based maintenanceChoose your Strategy
Preventive MaintenanceReactive Maintenance Predictive maintenance Optimised Maintenance
Data
Information
Knowledge
Action
Data
Information
Knowledge
Action
Data
Information
Knowledge
Action
Data
Information
Knowledge
Action
Knowledge Based Maintenance OptimisationBachmann Monitoring GmbH
CMS – On Line Vibration Monitoring
Vibration of turbine bearings; speed marker; process parameters
Converted into signals by sensors and fed to CMS hardware
Order related data; CVs trends; history; machine build
Analysed daily for long term trends
Current state of machine; cause of any anomalies; recommendations
Transmitted via weblog e-mails; stored in logs; work orders; reports
Data Information Knowledge
Knowledge based maintenance
Knowledge Based Maintenance OptimisationBachmann Monitoring GmbH
Diagnosis
Process of converting the information to knowledge
Commonly via a Fault/Symptom approach
Pattern of information associated with a particular fault – symptoms
Individual symptoms can indicate multiple faults
Identify patterns of multiple symptoms and corroborating information
Draws on previous experience
A good diagnosis will include
All fault modes considered possible and why
Which faults are considered most likely, and relative confidence
Reference to previous similar faults
A recommendation as to remedial action
Diagnosis turns information into knowledge
Idealised flow of Data, Information and Knowledge
Knowledge based maintenance
Bachmann Monitoring GmbH
Optimum Position of Managed Risk
Condition Monitoring
Risk Averse
Bachmann Monitoring GmbH
Condition Monitoring Monitoring of selected parameters to assess
the condition of the machine.
To be of use it must: Produce a measurable, non catastrophic
effect
Give sufficient warning time before failure
Be reproducible
Give a signature from which a diagnosis can be made
The ultimate aim: Provide a maintenance solution for the
plant which gives minimum cost and managed risk
Condition MonitoringWhy do we do it?
Bachmann Monitoring GmbH
Early detection of damage indications
More time to plan
Targets necessary maintenance
Avoids unnecessary maintenance
Informs customers about their machines
Allows OEM to be challenged
Enables operation under fault conditions
Reduces risk
SAVES MONEY
Condition MonitoringWhy we do it
Bachmann Monitoring GmbH
Defines the time between first possible detection (P) and functional failure (F)
Different for each machine and for each failure Mode
Essential part of the process definition – if PF is too short there is no point monitoring
Remember, P is where an incipient fault is detectable
Some conditions can be reset by maintenance, but most are the first sign of an impending failure
PF CurveDo we get enough warning?
Bachmann Monitoring GmbH
Temperature – either process parameters or
thermographic images
Pressures – either static or dynamic
Efficiency
Process Information
Vibration
Oil Analysis
Electrical Testing
Acoustic Emission
NDT
Not forgetting routine maintenance
inspections or whatever else is appropriate…
Suitable Parameters Include:
Condition Monitoring
Bachmann Monitoring GmbH
Development of a fault to failure
Trend Diagnosis
• High frequency energy only
• Limited riskStage 1
• Frequency spreads
• Bearing swap requiredStage 2
• Loss of shaft control
• Damage to journal/housing likelyStage 3
• No bearing related vibration energyNormal
Bachmann Monitoring GmbH
Trend DiagnosisIncreasing trend of an inner ring roll over frequency intermediate shaft bearing
April 20140,045g
March 20130,010g
June 20120,007g
Bachmann Monitoring GmbH
Cost Benefit Analysis
Electrical components may show a higher
failure rate but cause usually little
downtimes only
Failures on the mechanical components of
the drive train usually cause higher
downtimes per failure
Forwarning provides more planning time and allows maintenance to be optimised
Statistical failure rate vs. downtime rate (IWES)
Bachmann Monitoring GmbH
Versus Preventive Maintenance
Uses full life of parts – no replacement ofhealthy components
Reduced maintenance requirements
Reduced risk of maintenance inducedfailure
Fewer replacement parts
Versus Reactive Maintenance
Reduced downtime through planning
Reduced lost time due to weather
Parts ordered at right time, not kept on stock
Repair cheaper than replacement
Refurbishment of large items usuallypossible
No consequential damage
Cost Benefit AnalysisCondition Based Maintenance
Fraunhofer IWES, „Condition Monitoring of Wind Turbines“ 2015
Bachmann Monitoring GmbH
Analyse all possible failure modes
In each case calculate cost of:
undetected failure
routine replacement
planned replacement due to CM
Considering
Probability of failure
Probability of detection
Probability of maintenance induced failure
Aggregate across farm
Subtract monitoring costs (opex + capex)
Savings depend on assumptions
Most companies reluctant to share values
Academic studies suggest payback in between 1 and 3 years
Cost Benefit Analysis Theory
May, McMillan and Thöns, „Economic Analysis of CMS for offshore wind“ DTU at AWEA 2014
Bachmann Monitoring GmbH
Individual savings can be spectacular, but don‘t just take our word for it.
Sometimes system manufacturersoverstate the benefits
Operators and manufacturers don‘t like totalk about the actual savings, due tocommercial considerations (cost ofdowntime and cost of spares can becalculated)
Overall figures are hard to come by, but Almost all mayor WTG manufacturers have
adopted CMS as standard
Several operators now insist on CMS beingfitted.
Do you think it is worth it?
Cost Benefit AnalysisPractice
RES Website Case Study
Uniper Presentation at VGB Wind turbine Conference 2016
Bachmann CMS – An overall concept
Certified remote monitoring service in Europe , North America and Asia
Interface and installation through AGF Energia & Neo
Wind
Highest quality and customized CMS hardware based on Bachmann-own
production and development
Cabinet dimensions: 380x380x210mm
Direct mounting or installation via fixed bases ormagnets
Product line: Stand-Alone CMS
Mounting in control cabinet, controller-independent
Low expenses for hardware and low installation effort
Product line: Top-box CMS
BA
M1
00
• Sensitivity100 mV/g
• Output IEPE-compatible
• Measuring range0,5 Hz – 14 kHz
• recommendedfor monitoringfast rotatingcomponents
BA
M5
00
• Sensitivity500 mV/g
• Output IEPE-compatible
• Measuring range0,2 Hz – 14 kHz
• recommendedfor slow rotatingcomponents
µ-b
rid
ge
• Sensitivity0,7 V/N
• Output IEPE-compatible
• Measuring range0,05 - 1.000 Hz
• recommendedfor very slowrotatingcomponents
Product line: Sensors
Robust design
18 channels of ICP enabled vibration channels
1 speed signal input
3 Analogue inputs for process parameters
Browser-based configuration
Remote access
Set of sensors and cables available as an accessory pack
Based on same hardware as installed systems
Product line: Portable CMS
Best quality and availability by 100% test coverage and 48 h Run-In
Robustness even under extreme environmental conditions by using „ColdClimate“- modules
Open programmable standards IEC 61131-3, C/C++, Java, HTML5, Matlab/Simulink®
Long term availability by compatibility
Partnership relied on service and local support
Reduced Time-to-Market by system solutions
Designed for 20+ years life time
Committed to „Quality first“
Quality first
Bachmann CMS – Production
All Installation Instructions
Step by Step Process
Documented using built in camera
Certain fields mandatory
Automatically generatesEnd of Installation Protocol and e-mails it
Installation Tools
Several Forms Input & Choice Boxes Picture taken by Camera
Signature Save Mandatory EntriesInstant Data Transfer
Smartphone Application
Browser Based
Universally available
Access to tickets
Access to currentstatus
Access to simple Graphical Overview
Inclusive withMonitoring Service
Weblog
Software Capabilities
Client Based
Licensed Install
Access to databaseServer
Designed to Support Monitoring Process
Advanced Analysis Tools
ComprehensiveConfiguration Toolset
Weblog Expert
Software Capabilities
Highly Configurable:
Module
Channel
Acquisition
Classification
FFT Settings
Time Series
Filters
Thresholds
Logging
Weblog Expert - Configuration
Software Capabilities
Bachmann Monitoring GmbH
Blade rotor unbalance
Mass unbalance occurs if centre of mass and centre of rotation are not in the same place, eg due to a different Centre of mass between blades
Aerodynamic unbalance occurs if blades are not performing equally, eg due to pitch errors, or damage to the surface
Why is it important?
Blade mass centres cannot be perfectly matched, there will be a residual unbalance which needs correcting
Generally no balance quality is specified, or maybe G16 if any
In practice much better balance can be achieved, but is it?
Unbalance causes 1/ rev vibration which affects the entire structure ofthe turbine
Potential to shorten life of structural components and main bearings
Plug In to Bachmann CMS
Blade Unbalance Calculator
Bachmann Monitoring GmbH
Plug In to Bachmann CMS
Blade Unbalance Calculator
Blade rotor unbalance calculation on operating systems
No teams needed on the wind turbine
No need to interrupt productivity
Evaluates mass and aerodynamic unbalance
What we need?
Tower geometry and mass distribution
Tight rotor speed ranges for optimal results
10 to 20min measure time
RPM- and acceleration sensor (2D MEMS)
Optional: Angle of rotor axis for unbalance phase
Bachmann Monitoring GmbH
Blade rotor unbalance calculation on operating systems
Results generated on line during periods of stable operation at a specified speed range
Distinguishes between aerodynamic and mass unbalance
Calculates the result in kgm (distance from the centre is also important)
Calculates the phase if additional marker fitted
Benefits?
Assessment of residual unbalance on a regular basis
Early detection of out of tolerance machines before extreme values occur
On site focus can be on machines with problems
Answer for weight fitment requires no further trial run (with option)
Also detects pitch system errors
Plug In to Bachmann CMS
Blade Unbalance Calculator
Bachmann Monitoring GmbH
27 Datasets27.07.17-6.11.17
Unbalance Phase
Estimated by weight addition (3rd party)
376.1kgm 276°
Blade Unbalance CalculatorMean 377.6kgm 266°
First Results
Blade Unbalance Calculator
Polar histogram of phase
Also successfully detected aerodynamic unbalance on a test turbine
Case Studies Bachmann Monitoring GmbH
Case Study: Main Bearing Damage
Damage initiation seen at start of winter period
Damage progressed through winter
Under close monitoring
Repair in spring
Customer acted on knowledge
Advance warning provided
Ran on under close monitoring
Repair planned well in advance
Findings via µ-bridge sensor
First Warning
Repair
Progression
Case Studies Bachmann Monitoring GmbH
Case Study: Planetary Stage Tooth Damage
Early Detection of planetary stage damage
Trend in CV relating to TM frequency.
FFT indicated damage on the sunwheel
Levels and trend suggest progession likely
Customer informed of finding
Customer inspected gearbox
The sun gear was found to be „broken“
Gearbox exchange planned
Two weeks Advance Warning
Planetary Stage Catastrophic Failure avoided
Refurbishment of replacement of gearbox
Saving of €50k
Alert Raised 13.11.
Case Studies Bachmann Monitoring GmbH
Case Study: Helical stage gearbox
Sudden change in tooth mesh frequency
Customer Inspected gears
Early warning of tooth damage
Gear could be replaced
No consequential damage
Case Studies Bachmann Monitoring GmbH
Case Study: HSS Inner Ring Defect
• A HSS defect was noted
• Damage initiation possibly present when monitoring began
• First indication after the warning level was exceeded in February 2018
• Allowed the customer to plan a repair
• Intervention at ideal time
• Damage beginning to trend, but at this point minimal consequential damage
• Advance warning meant that the teams were ready to make the exchange with minimal loss of production when the damage began to increase
First Warning
Reminder
Repair
Case Studies Bachmann Monitoring GmbH
Case Study: Generator bearing
Bachman identified ball spin frequency in signal
Diagnosed roller damage
Progression relatively slow
Repair made when progression accelerated
Customer scheduled exchange when convenient
Spall from single ball
Can result from water in grease
Original material defect also possible
First Warning
Reminder
Repair
Case Studies Bachmann Monitoring GmbH
Case Study: Rotor Unbalance (Aerodynamic)
All aerodynamic unbalances corrected
Ongoing trial of mass unbalance system
Aerodynamic unbalance re-appeared after a few months
Customer requested more exact time for change
We identified a 6 hour window
Technicians on site applying loctite to pitch system
Rapid warning provided
Reduced loss of earnings
Customer: “That system rocks the party!“
Highest quality and customized hardware based on Bachmann-own production and development
Stand-alone solutions for any turbine make & model
Cost efficient integrated CMS for WTG with Bachmann controls
Portable CMS for offline diagnosis
PROVEN EXCELLENT QUALITY.
20 years of experience, more than 25 make and 80 models of wind turbines
Remote monitoring locations in China, Europe and USA
Cutting edge diagnostics from certified vibration analysts
Sophisticated software for efficient monitoring
Integration of data into SCADA
EXPERIENCE AND EFFICENCY.
Unique Quality, Experience, Efficiency out of one hand
Service by Bachmann CMS in WTG world wide distributeddata acquisition, analysing, threshold monitoring,automated data delivery to remote monitoring centre
customer, operator, maintenance team
CM REMOTE MONITORING CENTRE
evaluation, analysing, interpretation
reports with information for maintenance & logistics
data archiving, building a object database for monitored WTG for use of
LCC – optimized maintenance
support,
maintenance
and logistic
related
information
for use of
condition
based
maintenance
qualified support of CMS (HW, SW)
secured communication to world wide distributed CMS
Bro
wser
Ala
rm
DNV-GL-certified monitoring centre (20+ staff)
ISO 18436-2 certified vibration analysts
(CAT II, CAT III, CAT IV)
References Wind
600 kW – 8 MW (On- and Offshore)
Large and small O&M companies
Large and small utilities
Large and small OEM’s
Wind projects all over the world(from 2 to >1000 WTG)
Marine applications
Effektivwert
20 years experience
on WTG
More than
8 GW in CMS-
Monitoring
Verified on > 9000
WTG
Experience on 85
different gearbox
types
WTG
600kW to 8MW
Portfolio covers
more than 80 different WTG types
Onshore and
Offshore WTG
WTG portfolio covers 25
OEM
Experience on 10 drive train setups
>500 wind farms (from
2 WTG to 200 WTG)
World Nr. 1 CMS Solution Provider
Bachmann CMS – Experience counts
Logo Bachmann
Endereço Rio de Janeiro - RJRua Senador Dantas, 117, Salas 1742
Rio de Janeiro - RJ – BrasilTelefone: +55 21 3549 – 1669
Endereço Fortaleza - CERua Pinto Madeira 1500 - Sala 02
Fortaleza - CE / BrasilTelefone: +55 85 3033-0072